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Showing 1 to 5 of 5 for “"Crazyflie"”.

  1. L1 Crazyflie: An open-source testbed for robust adaptive control

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms

    uiuc Repository record for L1 Crazyflie: An open-source testbed for robust adaptive control (opens in a new tab)

  2. Nonlinear control strategies for quadrotors and CubeSats

    … for controlling the position and attitude of the Crazyflie quadrotor. In order to use this controller, a system identification is first performed in order to completely model the dynamics of the Crazyflie quadrotor. A proof of convergence is also given for this controller to show that the …

    uiuc Repository record for Nonlinear control strategies for quadrotors and CubeSats (opens in a new tab)

  3. In-Flight Testing & Verification of an Adaptive Distributed Fault-Tolerant Control Architecture

    … testbed consists of a multi-agent system of four Crazyflie 2.1 quadcopters, a VICON motion capture system for tracking the drones, and the Crazyswarm software architecture to send commands to the Crazyflie and receive data from the VICON system. In this thesis, a simulation environment …

    embry-riddle Repository record for In-Flight Testing & Verification of an Adaptive Distributed Fault-Tolerant Control Architecture (opens in a new tab)

  4. Ml Controllers With Memory For Robust Quadrotor Control And Research

    … SimToReal research. The system we will control a Crazyflie 2.X quadrotor drone, and its pose will be measured by the VICON motion capture system. We will be controlling the system with a neural network controller which is learned from a pyBullet simulation using the OpenAI Gym framework. Many …

    umn Repository record for Ml Controllers With Memory For Robust Quadrotor Control And Research (opens in a new tab)

  5. Human-aware Motion Planning for Aerial Robots

    <p>This project shows a combination for drones autonomous navigation in dynamic environments. The algorithm combine Social GAN SGAN for human trajectory prediction with Rapidly-exploring Random Tree Star RRT* for path planning. The objective is to efficiently and safe navigate in the area with …

    wustl Repository record for Human-aware Motion Planning for Aerial Robots (opens in a new tab)